Psychosocial Profile of Iranian Adolescents' Internet Addiction

Abstract

In the present study, factors that could play an important role in Internet addiction (IA) in 4,177 Iranian high school and secondary school adolescents (age range: 14–19 years) were examined. Data for the present study were gathered through Young's IA test, the General Health Questionnaire (GHQ), and family relationship questionnaires distributed between high school and secondary school students in different demographic regions, carefully selected using multistage sampling techniques. Among the study participants, 21.1% of the students were in some way victims of IA, among whom 1.1% had significant problematic symptoms. Familial relationships was the most important factor related to IA; religious beliefs, moreover, was the second most important factor. The father's level of education was more important than that of the mother's by nearly twice as much. Other factors had important roles in the kind of Internet use, but not as much as the above mentioned factors. The findings of this study could help parents, school counselors, and teachers to pay more attention to excessive Internet use in adolescents and propose possible solutions.

Introduction

Adolescence is a challenging period during which individuals experiment with the world around them, develop independent identities, and prepare for adulthood. With the widespread use of the Internet among adolescents, who comprise nearly 20% of Iran's 74 million population,¹ the negative impact of its excessive use and possible physical and psychological problems draw special attention. The problems associated with Internet overuse have raised special concerns in many other countries as well. It was in 1996 that the first research article on Internet addiction (IA) was published by Dr. Kimberly Young and presented at the American Psychological Association.² Since then, studying the physical and psychological consequences of Internet overuse has gained increasing importance, especially in China, Korea, and Taiwan where there are serious public health problems associated with IA.³ Fifty-six percent of the world's population and 44.8% of the world's Internet users live in Asia.⁴ Thus, IA could be a more important matter in Asia than in the rest of the world. There have been reports of students dying following Internet overuse in these countries (e.g., 10 reported cases of Korean teenagers who died of cardiac attack after playing online games continuously for hours).

A review of the literature reveals various names for IA, including cyberspace addiction, Internet addiction disorder, online addiction, net addiction, pathological Internet use, high Internet dependency, and so on, among which IA is the most popular.² IA is an obsessive behavior that might affect many aspects of life through one or all of the following activities: (a) relationships: spending excessive time in chatrooms, which replace real life friends and family; (b) money: compulsive online gambling, online trading, and partaking in online auctions; (c) information searching: compulsive web surfing or database searching; (d) gaming: obsessive computer game playing, including multiuser games; (e) sex: addiction to adult chatrooms, cybersex, or pornography on the Internet; (f) downloading: obsessive behavior of downloading media and images.^5–8

Internet addicts may suffer significantly in a number of ways. Studies demonstrate the relationship between Internet overuse and psychological disorders such as hyperactivity, conduct problems, psychosocial maladjustment, dependency and attachment, studying malfunction, and dysfunctional coping strategies.^9–13 Social and familial problems such as a feeling of social belonging, low self-esteem, low quality of familial relationships, and low quality of interpersonal relations are also caused by overusing the Internet.^13–15 Anxiety disorders (generalized and social anxiety disorder) and attention deficit hyperactivity disorder (ADHD) are seen in Internet addicts.^15,16 Internet addicts also have a higher chance of other addictions, such as alcohol and drug addictions,^17,18 and a higher chance of suicidal ideation.¹⁹ Moreover, several physical health problems such as dry eyes, migraine headache, backache, and weight gain are diagnosed in Internet addicts.^2,12,17

There are quite a number of factors that affect the quality and quantity of Internet use in adolescence. These factors depend on the structure of the families, and vary among countries with different cultures, political and social situations such as taboos, discriminations, attitudes toward sex, and restrictions applied by governments. In Iran, for instance, web pages with sexual or pornographic content are blocked, and use of virtual private networks (VPNs) and/or applications for bypassing web content filtering will be prosecuted.

Up to now, some researchers have tried to propose the factors related to IA in some countries.^2,14,20–22 The aim of the present study is to determine important familial, cultural, and personal factors associated with IA in Iranian adolescents. To the best of our knowledge, no other article has focused on this subject with the amount of detail that we have. Therefore, the hypothesis of this study was:

H1: There are multiple factors related with IA in Iranian adolescents, such as personal, psychological, family, and religiosity.

Materials and Methods

The study population of this investigation is composed of 4,177 Iranian students from high schools and secondary schools, aged from 14 to 19 years. The sample size was determined using Krejcie and Morgan's table.²³ The sample itself was selected using the multistage cluster sampling technique: in the first phase, 12 provinces out of 30 provinces in Iran were selected; in the second phase, two schools were chosen from each province considering the geographical dispersion; finally, students in each school were selected randomly using systematic sampling to participate in this study (2,091 males; 2,086 females). The present study was approved by the ethical committee of the Baqiyatallah University of Medical Sciences, and all study participants signed informed consent forms.

The survey consisted of three paper-and-pencil questionnaires. The individual questionnaire, which had 50 items, gathered demographic information, familial and academic status, religious beliefs, and quantity–quality use of virtual spaces. This questionnaire was used with a small sample of students, producing an internal consistency of 0.88 derived using Cronbach's alpha. Some of the items included “How much are your parents familiar with Internet?”; “How much can you access the filtered web pages?”; “How much do you have access to broadband Internet?”; “How much do you bound yourself to religious activities such as praying?”; “How much are you familiar with the Quran?”; “How much do you consider yourself a religious person?”; and “How much do you participate in sports?”. Each item was accompanied by five possible responses—“not at all,” “low,” “moderate,” “much,” and “very much”—scoring from 0 to 4 respectively.

The level of IA was assessed using Young's IA Test (IAT). The IAT has been shown to provide a valid scale assessing the presence and severity of IA.²⁴ Thus, it was carefully translated into Persian with the help of translators and professors of the Behavioral Sciences Research Center and used in this study. The IAT comprises 20 items that measure mild, moderate, and severe levels of IA. Each item is accompanied by six possible responses—“does not apply,” “never,” “rarely,” “occasionally,” “often,” and “always”—scoring from 0 to 5 respectively. The total possible score ranges from 0 to 100; the higher the score, the greater the level of addiction. Scores <19 were deemed nonadequate Internet users and were removed from further analysis; scores ranging from 20 to 49 indicated an average online user; scores ranging from 50 to 79 implied occasional or frequent problematic users; and scores >80 implied users with significant problems.²

The General Health Questionnaire with 28 items (GHQ-28) has been used for mental health assessment on four dimensions: somatic symptoms (items 1–7), anxiety and insomnia (items 8–14), social dysfunction (items 15–21), and severe depression (items 22–28). Each item has four possible responses, typically being “not at all,” “no more than usual,” “rather more than usual,” and “much more than usual.” A “0, 0, 1, 1” scoring scheme for respective responses allows the test to range scores from 0 to 28 for each individual. Any score >4 indicates the presence of distress. Note that the GHQ-28 was developed by Goldberg in 1978,²⁵ and since then it has been translated into at least 38 different languages. It has been used in numerous studies to screen for emotional distress and possible psychiatric morbidity,²⁶ and its reliability coefficients have ranged from 0.78 to 0.95. A translated version of GHQ-28 has also been tested in Iran, and its validity, internal consistency (α=0.91), test–retest reliability (r=0.79), and bisection reliability (r=0.93) have also been approved.

Statistical analysis was carried out using SPSS for Windows, version 19. Descriptive and chi square (χ²) tests were used, and p values<0.05 were assumed as significant.

Results

Thirty-six male and 46 female students were dropped from analyses but not from descriptive statistics because they failed to complete the IAT and/or GHQ. Basic results showed that the average daily Internet use was 1.4 hours, 7 hours per week. Ten percent of the adolescents reported using the Internet more than 3 hours a day, 15 hours per week. As expected, most of the adolescents reported using the Internet at weekends (62%), while the rest were equally likely to be online on weekdays. Among participants, 3% reported using the Internet on a daily basis, and moreover educational (33.8%) and entertainment (33%) purposes were the main reasons of using the Internet. Social (9.7%), religious (8.3%), political (7.6%), and other purposes (7.6%) stood third to fifth. The most preferred hours of the day for surfing the web were afternoons and nights, which were equally as likely at 40%. Furthermore, 10% reported using the Internet before noon, and 10% after midnight.

Similar to the recent work by Austina and Totaro,²⁷ our findings demonstrated significant differences in the quality of Internet use by male and female students (Table 1). Female students spent less of their online time in chatrooms and social networks than males. They also used the Internet for downloading, sexual subjects, and privately less than male students. Around 17.8% of the test subjects were able to access the filtered web pages easily, and 22% had access to broadband Internet more often. Table 1 also shows that female students spent their time more in chatrooms than any other Internet activity.

Table 1.

Quality/Quantity of Internet Use Among Male/Female Students

	Not at all	Low	Moderate	Much	Very much	Total	p Value
How well can you access filtered web pages?
Female	974	469	310	127	85	1,965	<0.0001
Male	774	368	317	256	227	1,942
Total	1,748	837	627	383	312	3,907
How much do you have access to broadband Internet?
Female	790	458	394	220	177	2,039	0.001
Male	802	389	361	261	235	2,048
Total	1,592	847	755	481	412	4,087
How much do you spend time in chatrooms?
Female	1,288	333	230	110	106	2,067	<0.0001
Male	1,139	393	240	147	135	2,054
Total	2,427	726	470	257	241	4,121
How much do you use social networks?
Female	1,352	312	212	91	94	2,061	<0.0001
Male	1,145	301	283	167	159	2,055
Total	2,497	613	495	258	253	4,116
How much do use the Internet privately?
Female	1,446	311	139	91	86	2,073	<0.0001
Male	970	445	286	183	181	2,065
Total	2,416	756	425	274	267	4,138
How much do use the Internet for downloading?
Female	1,385	336	160	85	90	2,056	<0.0001
Male	1,058	446	234	162	158	2,058
Total	2,443	782	394	247	248	4,114
How much do use the Internet for sexual subjects?
Female	1,595	224	114	65	89	2,087	<0.0001
Male	1,245	280	179	164	209	2,077
Total	2,840	504	293	229	298	4,164

Table 2 provides the results of the IAT versus some demographic variables. The prevalence rates of the three categories of IA (average online users, occasional or frequent addictive users, and significant addictive users) were 78.9%, 20%, and 1.1% respectively. Specifically, male adolescents tended to have more addictive behavior than females (χ²=85.285, p<0.0001). There was a significant association with age as well (χ²=64.946, p<0.0001). The gamma coefficient for the association between age and IAT results was 0.162 (p<0.0001), which shows a higher risk of addiction with increasing age.

Table 2.

Internet Addiction Test Results for Different Factors

			Young's category
	Variables	n	Average online user	Occasional or frequent addictive user	Significant addictive user
Sex	Female	2,066	1,748	296	22
	Male	2,006	1,464	519	23
Age	14	231	203	25	3
	15	771	637	123	11
	16	1,068	854	202	12
	17	1,163	903	252	8
	18	754	572	174	8
	19	108	60	45	3
Education level	1st year high school	820	642	163	15
	2nd year high school	964	773	182	9
	3rd year high school	1,000	800	193	7
	1st year secondary school	865	665	193	7
	2nd year secondary school	16	10	5	1
	3rd year secondary school	5	3	2	0
Number of siblings	0	145	113	28	4
	1	1,186	988	189	9
	2	1,136	916	207	13
	3	630	489	136	5
	≥4	903	650	240	13

Moreover, the gamma coefficients for level of education and number of siblings were 0.026 and 0.163 respectively, with 0.385 and 0.0001 significance levels. This showed that level of education was not significantly associated with IA. However, the number of siblings was associated with adolescents' IA. This result was expected, since with an increase in family members, parental control becomes less effective.

All variables listed in the familial relationships were associated with adolescents' IA. Table 3 shows that the association of the father's and mother's education level and the number of siblings with IA was direct, and it therefore increases with them. Since family wealth was scored from 1 to 5 for “very much” to “very low,” a negative gamma coefficient showed an increase in addiction level with a decrease in family wealth. Moreover, the quality of the familial relationship was scored from 1 to 5 for “very close” to “with arguments.” It was strongly associated with the level of addiction (χ²=126.518, p<0.0001), and with diminishing family bonds, the level of IA increases by 29.6%. Similarly, with a decreasing bond between one's friends and teachers, IA levels increase. These results are in agreement with the work of Bellamy and Hanewitz.²⁸

Table 3.

Chi Square Test and Gamma Coefficient for Some Familial Variables

Variable	Test	Value	Asymp. Sig.
Father's education	Pearson χ²	34.322	0.0006
	Gamma	0.084	0.002
Mother's education	Pearson χ²	68.977	<0.0001
	Gamma	0.104	<0.0001
Family wealth	Pearson χ²	61.337	<0.0001
	Gamma	−0.101	<0.002
Familial relationship	Pearson χ²	126.518	<0.0001
	Gamma	0.296	<0.0001
Relationship with friends and teachers	Pearson χ²	44.821	<0.0001
	Gamma	0.044	0.143
Number of siblings	Pearson χ²	47.316	<0.0001
	Gamma	0.163	<0.0001

All three religious variables seem to be correlated with the level of IA, and surprisingly their influence is more powerful than the familial variables (Table 4, gamma coefficients). Since these variables were scored from 1 to 5 for “very much” to “not at all,” using gamma coefficients, one can infer that the level of addiction increases with the increasing level of response. On the other hand, adolescents with stronger religious beliefs tend to be less affected by IA. Sport is scored in the same way as religious beliefs, and is thus presented in the same table. The results show that adolescents who participated in sporting activities had a lower level of IA (the gamma coefficient is negative).

Table 4.

Chi Square Test and Gamma Coefficient for Religious Variables and Sport

Variable	Test	Value	Asymp. Sig.
Praying	Pearson χ²	99.744	<0.0001
	Gamma	0.247	<0.0001
Familiar with Quran	Pearson χ²	94.61	<0.0001
	Gamma	0.15	<0.0001
Consider yourself religious	Pearson χ²	115.843	<0.0001
	Gamma	0.128	<0.0001
Participate in sports	Pearson χ²	55.449	<0.0001
	Gamma	−0.081	0.005

Overall, 1,782 adolescents suffered from various types of health problems (Table 5). A chi square test on the distribution of GHQ results over Young's IAT categories revealed that the two factors were highly correlated. The value of the chi square test was 263.709 with two degrees of freedom, which is strongly significant. Moreover, the gamma coefficient of was 0.556 and strongly significant. This means that with increasing levels of IA, the chance of having some kind of health issues increases by 55.6%.

Table 5.

Young's Questionnaire Versus General Health Questionnaire (GHQ) Results

		Young's category
Count		Average online user	Occasional or frequent problematic user	Significant problematic user	Total
GHQ result	Healthy	2,034	262	17	2,313
	Presence of distress	1,195	559	28	1,782
Total		3,229	821	45	4,095

To find out which dimension of mental health is affected by overuse of the Internet, we repeated the previous analysis, this time on different aspects of mental health. Table 6 shows the IA categories versus the GHQ results on four different mental aspects. Since the scores on each category were not distributed normally, we used Kruskal–Wallis median rank test. The medians of three IA levels were different in all four mental health aspects, and the p value for all four Kruskal–Wallis tests showed a significant difference in medians as well. The post hoc tests revealed the same order of associations between mental health factors. For example, in somatic symptoms, the median of the level 1 IA scale was less than the median of the level 2 IA, but in levels 2 and 3, the difference in medians was not statistically significant. The tests here were valid for any significance level >0.001 except for the difference in levels 1 and 3 of the social dysfunction aspect, which was valid for any significance level >0.04 (marked by an asterisk in Table 6). We inferred from these findings that all four general health aspects, that is, somatic–anxiety–social–depression, were affected by the severity of IA.

Table 6.

Influence of General Health on Different Internet Addiction Categories

		Young's category
GHQ dimension	Statistics	Average online user (A)	Occasional or frequent problematic user (B)	Significant problematic user (C)	Total	Difference between three groups	Post hoc
Somatic symptoms	Mean	0.96	2.22	2.60	1.23	p<0.001	A<B, A<C
	Median	0.00	2.00	2.00	0.00		B=C
Anxiety and insomnia	Mean	1.24	2.46	2.89	1.51	p<0.001	A<B, A<C
	Median	0.00	2.00	3.00	1.00		B=C
Social dysfunction	Mean	1.29	2.20	2.04	1.48	p<0.001	A<B, A<C^*
	Median	1.00	2.00	1.00	1.00		B=C
Severe depression	Mean	1.28	2.52	2.73	1.54	p<0.001	A<B, A<C
	Median	0.00	2.00	2.00	0.00		B=C

Discussion

The aim of this study was to assess and examine the pervasive factors in Internet use and addiction in Iranian adolescents. With Iran's 5th development program, the government is trying to provide the groundwork for the latest information technology and communication systems. The improvement in the banking system during past few years is one of the better examples of this. Use of the Internet has become a necessity in the everyday life of so many people. The Internet penetration rate (IPR), which is computed as the number of Internet users across the population, is a comparison tool to study each country's access level to the World Wide Web. Sweden (85%), Denmark (83%), Iceland (82%), South Korea (82%), the Netherlands (79%), China (79%), Finland (79%), Taiwan (79%), Canada (78%), and the United States (78%) are the top ten countries with the highest IPR. Iran, with an IPR of 43%, is the 86th country. However, the IPR growth rate of 9.1% over the last 5 years shows a great increase in the number of Internet users in Iran. Among them, Iranian adolescents play an important role, since they are the link between new technologies and the former old fashioned generation.

Filtering is being taken seriously in Iran, such that Iran, after China, has the most filtered web pages worldwide. On the other hand, there are pages that cannot be opened in Iran due to some regulations (e.g., the Android Market and some pages from the United States). But this does not completely restrict Internet users, since they can use various tools such as Proxy Servers or VPNs to bypass these limitations.

Our study revealed that 21.1% of students were in some way victims of IA, among whom 1.1% had significant problematic symptoms. The problematic IA rate for Iranian adolescents was lower than that of the Chinese (8.1%) and Koreans (4.3%).^3,29 However, considering the groundwork of information technology in these countries, this seems worrying. We found familial relationships to be the most important factor in IA, while religious beliefs were the second most important. Therefore, they are very much correlated with Internet overuse. Significantly, adolescents who overuse the Internet more have some problems with their families. These adolescents perceived that the Internet could help them and that they could find friends online. Some studies, however, have argued that the Internet removes the human touch from our social interactions and thus increases the sense of isolation.^14,30

Parents' education level was the third most important factor on Iranian adolescents' IA. The father's level of education was more important than that of the mother though. Other factors such as family wealth, relationship with friends and teachers, and number of siblings were also important in determining the extent of Internet use, but not as much as the above mentioned factors.

Clearly, IA has a close association with the general health of these adolescents. The severity of their health problems elevated with the increase in their level of addiction. Moreover, the addicted users suffered from all four aspects of general health. These results are in agreement with recent studies on the effects of IA on adolescents. Kelleci et al.¹¹ in their study on high school students in Sivas, Turkey, reported significant correlations between Internet use and psychiatric symptoms. In other words, the behaviors related to IA may be symptoms of depressive and anxiety disorders in adolescents. Moreover, behavioral, interpersonal, educational, and psychological problems and maladjustment cause Internet abuse, and vice versa.^21,22

It is important to help youngsters become accustomed to the Internet by limiting and balancing the time they spent online with other activities. Seeking professional counseling for adolescents who use the Internet excessively is an option when they demonstrate strong signs of IA. This is extremely important, since IA damages adolescents mentally as well as socially and physically. Ha et al.³¹ found that one third of addicted subjects in their study had a significant level of depressive and suicidal symptoms that required psychiatric intervention. There are good resources that can guide parents and counselors to help adolescents overcome their IA.³²

The religion of Iranians is Islam, and in Islam, pornography is considered illicit. Therefore, for some adolescents interested in obviating sexual needs with pornography, the Internet brings them more opportunities. Based on this, we predict that the use of the Internet in IA adolescents is likely related to sexual behavior.^33,34

While this is an important study that identified the association of several factors with adolescents' IA, considering its correlational design, causational inferences cannot be made. Nevertheless, these factors are potentially influential in IA and should not be neglected. We hope the results of this study help parents, school counselors, and teachers to pay more attention to excessive Internet use in adolescents, and propose possible solutions.

Footnotes

Acknowledgments

This study was supported by the Behavioral Sciences Research Center, Baqiyatallah University of Medical Sciences. The authors would like to thank its students and staff.

Author Disclosure Statement

No competing financial interests exist.

References

Iran's Statistical Training & Research Center, the Annual report of the Iran's Statistical Training & Research Center. 2011.

Young

. Internet addiction: the emergence of a new clinical disorder. Cyber Psychology & Behavior, 1998; 1:237–44.

Hui

Cao

, Ying

Sun

, Yuhui

Wan

et al. Problematic Internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health, 2011; 11:802–10.

Internet World Stats. www.internetworldstats.com/(2012 Sept 1.)

Aviv

, Michael

. Internet addiction or excessive Internet use. American Journal of Drug & Alcohol Abuse, 2010; 36:277–83.

Ferris

. 2003. Internet addiction disorder: cause, symptoms, and consequences. www.rider.edu/∼suler/psycyber/cybaddict.html(2012 Sept 1).

Lam

, Peng

, Mai

et al. Factors associated with Internet addiction among adolescents. CyberPsychology & Behavior, 2009; 12:551–5.

Tsitsika

, Critselis

, Janikian

et al. Association between Internet gambling and problematic Internet use among adolescents. Journal of Gambling Studies, 2007; 27:389–400.

Chiungjung

. Internet use and psychological well-being: a meta-analysis. CyberPsychology, Behavior, & Social Networking, 2010; 13:241–9.

10.

Kormas

, Critselis

, Janikian

et al. Risk factors and psychosocial characteristics of potential problematic and problematic Internet use among adolescents: a cross-sectional study. Public Health, 2011; 11:595–603.

11.

Kelleci

, Inal

. Psychiatric symptoms in adolescents with Internet use: comparison without Internet use. CyberPsychology, Behavior, & Social Networking, 2010; 13:191–4.

12.

Lawrence

, Peng

, Mai

et al. Factors associated with Internet addiction among adolescents. Cyber Psychology & Behavior, 2009; 12:551–5.

13.

Cao

, Su

. Internet addiction among Chinese adolescents: prevalence and psychological features. Child Care Health and Development, 2007; 33:275–81.

14.

Yen

, Yen

, Chen

et al. Family factors of Internet addiction and substance use experience in Taiwanese adolescents. Cyber Psychology & Behavior, 2007; 10:323–9.

15.

Luca

, Dania

, Paola

. Quality of interpersonal relationships and problematic Internet use in adolescence. CyberPsychology & Behavior, 2009; 12:681–684.

16.

, Yen

et al. Factors predictive for incidence and remission of Internet addiction in young adolescents: a prospective study. CyberPsychology & Behavior, 2007; 10:545–51.

17.

Fisoun

, Floros

, Siomos

et al. Internet addiction as an important predictor in early detection of adolescent drug use experience—implications for research and practice. Journal of Addiction Medicine, 2012; 6:77–84.

18.

, Yen

et al. The association between Internet addiction and problematic alcohol use in adolescents: the problem behavior model. CyberPsychology & Behavior, 2008; 11:571–6.

19.

Kim

, Ryu

, Chon

et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. International Journal of Nursing Studies, 2006; 43:185–92.

20.

Suhail

, Bargees

. Effects of excessive Internet use on undergraduate students in Pakistan. CyberPsychology & Behavior, 2006; 9:297–307.

21.

Tsai

, Lin

SSJ

. Internet addiction of adolescents in Taiwan: an interview study. CyberPsychology & Behavior, 2004; 6:649–52.

22.

Yang

, Tung

. Comparison of Internet addicts and non-addicts in Taiwanese high school. Computers in Human Behavior, 2004; 23:79–96.

23.

Krejcie

, Morgan

. Determining sample size for research activities. Educational & Psychological Measurement, 1970; 30:607–10.

24.

Widyanto

, McMurran

. The psychometric properties of the Internet addiction test. CyberPsychology & Behavior, 2004; 7:443–50.

25.

Goldberg

. Manual of the General Health Questionnaire. Windsor, England: NFER Publishing, 1978.

26.

Jackson

. The General Health Questionnaire. Occupational Medicine, 2007; 57:79.

27.

Austina

, Totaro

. Gender differences in the effects of Internet usage on high school absenteeism. The Journal of Socio-Economics, 2011; 40:192–8.

28.

Bellamy

, Hanewicz

. An exploratory analysis of the social nature of Internet addiction. Electronic Journal of Sociology, 2001; 5. www.sociology.org/content/vol005.003/ia.html(2012 Sept 1).

29.

Jang

, Hwang

, Choi

. Internet addiction and psychiatric symptoms among Korean adolescents. Journal of School Health, 2008; 78:165–71.

30.

Campbell

, Cumming

, Hughes

. Internet use by the socially fearful: addiction or therapy? CyberPsychology & Behavior, 2006; 9:69–81.

31.

, Yoo

, Cho

et al. Psychiatric co morbidity assessed in Korean children and adolescents who screen positive for Internet addiction. Journal of Clinical Psychiatry, 2006; 67:821–6.

32.

Young

. The center for Internet addiction, Digital Detox™ program. www.netaddiction.com/(2012 Sept 1).

33.

Ahmadi

, Rezazade

, Nafarie

et al. Unprotected sex with injecting drug users among Iranian female sex workers: unhide HIV risk study. AIDS Research and Treatment, 2012Epub 2012 Mar 1910.1155/2012/651070

34.

Ahmadi

, Ranjebar-Shayan

, Raiisi

. Sexual dysfunction and marital satisfaction among the chemically injured veterans. Indian Journal of Urology, 2007; 23:377–82.